Shinichi-Nakagawa/shohei_otani_pitching_stats.ipynb

## shohei_otani_pitching_stats.ipynb
{
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   "execution_count": 1,
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   "source": [
    "%matplotlib inline\n",
    "# データを取ってスクレイピングする\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "# 表示するカラムサイズを指定\n",
    "pd.options.display.max_columns = 60\n",
    "\n",
    "url = 'http://npb.jp/bis/players/01305137.html'  # 察してください\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datasets = pd.io.html.read_html(url)  # データを読む&スクレイピング(たったこれだけ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 前処理用の関数を定義します\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "def object_2_float64(df, index):\n",
    "    # 欠損値(多分無いと思うが...)をゼロ埋めしてからのfloat64に変換\n",
    "    return df[index].fillna(0).astype(np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 投手成績を取るよ&ヘッダー行と通算行を削除\n",
    "pitchs = datasets[3].drop([0, len(datasets)-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 計算可能なデータ型に変換\n",
    "for index in range(2, 25):\n",
    "    pitchs[index] = object_2_float64(pitchs, index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>北海道日本ハム</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>274.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>57.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>4.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>北海道日本ハム</td>\n",
       "      <td>24.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.733</td>\n",
       "      <td>639.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>125.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>北海道日本ハム</td>\n",
       "      <td>22.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.750</td>\n",
       "      <td>621.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>北海道日本ハム</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.667</td>\n",
       "      <td>428.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>72.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0        1     2     3    4    5    6    7    8    9    10     11     12  \\\n",
       "1  2013  北海道日本ハム  13.0   3.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.000  274.0   \n",
       "2  2014  北海道日本ハム  24.0  11.0  4.0  0.0  0.0  0.0  3.0  2.0  0.0  0.733  639.0   \n",
       "3  2015  北海道日本ハム  22.0  15.0  5.0  0.0  0.0  0.0  5.0  3.0  0.0  0.750  621.0   \n",
       "4  2016  北海道日本ハム  15.0   8.0  4.0  0.0  0.0  0.0  3.0  0.0  1.0  0.667  428.0   \n",
       "\n",
       "      13   14     15   16    17   18     19   20   21    22    23    24  \n",
       "1   61.0  0.2   57.0  4.0  33.0  8.0   46.0  2.0  0.0  30.0  29.0  4.23  \n",
       "2  155.0  0.1  125.0  7.0  57.0  4.0  179.0  6.0  1.0  50.0  45.0  2.61  \n",
       "3  160.0  0.2  100.0  7.0  46.0  3.0  196.0  9.0  0.0  40.0  40.0  2.24  \n",
       "4  108.0  0.2   72.0  4.0  37.0  7.0  132.0  4.0  0.0  26.0  23.0  1.90  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 中身をチェック\n",
    "pitchs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     61.2\n",
       "2    155.1\n",
       "3    160.2\n",
       "4    108.2\n",
       "Name: IP, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 指標値を計算...の前に投球回が別れて出ちゃってるのでくっつけます\n",
    "pitchs['IP'] = pitchs[13] + pitchs[14]\n",
    "pitchs['IP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>K/9</th>\n",
       "      <th>BB/9</th>\n",
       "      <th>HR/9</th>\n",
       "      <th>K/BB</th>\n",
       "      <th>WHIP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>6.764706</td>\n",
       "      <td>4.852941</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>1.393939</td>\n",
       "      <td>1.470588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>10.386847</td>\n",
       "      <td>3.307544</td>\n",
       "      <td>0.406190</td>\n",
       "      <td>3.140351</td>\n",
       "      <td>1.173436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>11.011236</td>\n",
       "      <td>2.584270</td>\n",
       "      <td>0.393258</td>\n",
       "      <td>4.260870</td>\n",
       "      <td>0.911361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>10.979667</td>\n",
       "      <td>3.077634</td>\n",
       "      <td>0.332717</td>\n",
       "      <td>3.567568</td>\n",
       "      <td>1.007394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0        K/9      BB/9      HR/9      K/BB      WHIP\n",
       "1  2013   6.764706  4.852941  0.588235  1.393939  1.470588\n",
       "2  2014  10.386847  3.307544  0.406190  3.140351  1.173436\n",
       "3  2015  11.011236  2.584270  0.393258  4.260870  0.911361\n",
       "4  2016  10.979667  3.077634  0.332717  3.567568  1.007394"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 試しに計算\n",
    "pitchs['K/9'] = pitchs[19] * 9 / pitchs['IP']\n",
    "pitchs['BB/9'] = pitchs[17] * 9 / pitchs['IP']\n",
    "pitchs['HR/9'] = pitchs[16] * 9 / pitchs['IP']\n",
    "pitchs['K/BB'] = pitchs[19] / pitchs[17]\n",
    "pitchs['WHIP'] = (pitchs[15] +  pitchs[17]) / pitchs['IP']\n",
    "pitchs[[0, 'K/9', 'BB/9', 'HR/9', 'K/BB', 'WHIP']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1146a5668>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112d06e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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L8Xo5ikqVsPXEJ1fp8Pv1e/j9+j3uWJCfxDwpL8wPsV38EBnqC6nYI9M3CCGE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\n9YoQQsjtxK/Afvfdd7utLx88eLDb44YCuzezZs2CTCYDAIwYMQKXL19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5VuCtLzBXP/Y2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112d06e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 年度ごとのグラフにしてみる\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(20,10))\n",
    "fig, ax = plt.subplots(5);\n",
    "sns.pointplot(x=0, y=\"K/9\", data=pitchs, ax=ax[0])\n",
    "sns.pointplot(x=0, y=\"BB/9\", data=pitchs, ax=ax[1])\n",
    "sns.pointplot(x=0, y=\"HR/9\", data=pitchs, ax=ax[2])\n",
    "sns.pointplot(x=0, y=\"K/BB\", data=pitchs, ax=ax[3])\n",
    "sns.pointplot(x=0, y=\"WHIP\", data=pitchs, ax=ax[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}